Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
권호 표지
DOI QR코드

DOI QR Code

Texture-aware Blur Detection

질감 특징을 고려한 영상 흐려짐 검출 방법

  • Jeong, Chanho (Department of Electrical and Electronics Engineering, Konkuk University) ;
  • Kim, Wonjun (Department of Electrical and Electronics Engineering, Konkuk University)
  • 정찬호 (건국대학교 전기전자공학부) ;
  • 김원준 (건국대학교 전기전자공학부)
  • Received : 2019.09.16
  • Accepted : 2019.11.05
  • Published : 2020.01.30
Download PDF
⟨ Previous Next ⟩

Abstract

The blur effect, which is generated by various external factors such as out-of-focus and object movement, degrades high-frequency components in the original sharp image. Based on this observation, we propose a novel method for blur detection using textural features. Specifically, the proposed method simultaneously adopts learning-based and watershed-based textural features, which effectively detect the blur on various situations. Moreover, we employ the region-based refinement to improve the processing time while also increasing detection accuracy. Experimental results demonstrate that the proposed method provides the competitive performance compared to previous approaches in literature.

영상 촬영 시 객체의 움직임, 탈초점(Out-of-focus) 등의 이유로 영상 흐려짐 현상이 빈번하게 발생하며, 이 과정에서 선명한 영역의 고주파 성분이 급격하게 감소하게 된다. 이러한 성질을 바탕으로, 본 논문에서는 질감 특징 표현자를 사용하여 별도의 주파수 변환 과정 없이 고주파 성분을 추정하고, 이를 바탕으로 흐려진 영역을 검출하는 방법을 제안한다. 제안하는 방법은 학습 기반 질감 표현자와 유역(Watershed) 기반 질감 표현자를 함께 이용하여 다양한 환경에서도 흐려진 영역을 검출할 수 있다. 또한, 흐려짐을 검출하는 최소 단위를 화소 단위에서 영역 단위로 확장하여 처리 속도를 향상시키고, 영상 보정 기법을 이용하여 흐려짐 검출 성능을 개선하였다. 실험 결과는 제안하는 방법이 기존의 흐려짐 검출 방법 대비 성능이 향상되었음을 보여준다.

Keywords

References

  1. A. Chakrabarti, T. Zickler, and W. T. Freeman, "Analyzing spatially-varying blur," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2010, pp. 2512-2519.
  2. J. Shi, L. Xu, and J. Jia, "Just noticeable defocus blur detection and estimation," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2015, pp. 657-665.
  3. J. Shi, L. Xu, and J. Jia, "Discriminative blur detection features," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2014, pp. 2965-2972.
  4. S. Zhuo and T. Sim, "Defocus map estimation from a single image," Pattern Recognit., vol. 44, no. 9, pp. 1852-1858, Sep. 2011. https://doi.org/10.1016/j.patcog.2011.03.009
  5. S. El-Shekheby, R. F. Abdel-Kader and F. W. Zaki, "Spatially varying blur estimation from a single image," IET Image Process., vol. 13, no. 5, pp. 746-753, Apr. 2019. https://doi.org/10.1049/iet-ipr.2018.5663
  6. T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971-987, Jul. 2002. https://doi.org/10.1109/TPAMI.2002.1017623
  7. S. Beucher and F. Meyer, "The morphological approach to segmentation: The watershed transform," in Mathematical Morphology in Image Processing., E. R. Dougherty, Ed. New York: Marcel Dekker, vol. 12, pp. 433-481, 1993.
  8. X. Yi and M. Eramian, "LBP-based segmentation of defocus blur," IEEE Trans. Image Process., vol. 25, no. 4, pp. 1626-1638, Apr. 2016. https://doi.org/10.1109/TIP.2016.2528042
  9. Chanho Jeong and Wonjun Kim, "High-speed blur detection based on the textural features," in Proc. Workshop on Image Processing Image Understanding, Feb. 2019.
  10. J. Kannala and E. Rahtu, "BSIF: Binarized statistical image features," in Proc. IEEE Int. Conf. Pattern Recognit., Nov. 2012, pp. 1363-1366.
  11. R. Huang, M. Fan, Y. Xing and Y. Zou, "Image blur classification and unintentional blur removal," IEEE Access, vol. 7, pp. 106327-106335, Jul. 2019. https://doi.org/10.1109/ACCESS.2019.2932124
  12. X. Wang, S. Zhang, X. Liang, H. Zhou, J. Zheng and M. Sun, "Accurate and Fast Blur Detection Using a Pyramid M-Shaped Deep Neural Network," IEEE Access, vol. 7, pp. 86611-86624, Jul. 2019. https://doi.org/10.1109/ACCESS.2019.2926747
  13. C. Pierre, "Independent Component Analysis: a new concept?," Signal Process., vol 36, no. 3, pp. 287-314, Mar. 1994. https://doi.org/10.1016/0165-1684(94)90029-9
  14. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, "SLIC superpixels compared to state-of-the-art superpixel methods," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2274-2282, Nov. 2012. https://doi.org/10.1109/TPAMI.2012.120
  15. C. Tang, J. Wu, Y. Hou, P. Wang, and W. Li, "A spectral and spatial approach of coarse-to-fine blurred image region detection," IEEE Signal Process. Lett., vol. 23, no. 11, pp. 1652-1656, Nov. 2016. https://doi.org/10.1109/LSP.2016.2611608
  16. C. Tang, C. Hou, and Z. Song, "Defocus map estimation from a single image via spectrum contrast," Opt. Lett., vol. 38, no. 10, pp. 1706-1708, May 2013. https://doi.org/10.1364/OL.38.001706